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RNA Splicing

RNA Splicing

Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...

RNA Splicing

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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

Alternative RNA Splicing

Alternative RNA Splicing

Alternative RNA splicing is the regulated splicing of exons and introns to produce different mature mRNAs from a single pre-mRNA. Unlike in constitutive splicing where a single gene produces a single type of mRNA, alternative splicing allows an organism to produce multiple proteins from a single gene and plays an important role in protein diversity.
There are five types of alternative RNA splicing that vary in the ways the pre-mRNA segments are removed or retained in the mature mRNA. The first...

Alternative RNA Splicing

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Updated: Apr 20, 2026

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Published on: December 9, 2016

Mapping Splicing Quantitative Trait Loci in RNA-Seq.

Cheng Jia¹, Yu Hu¹, Yichuan Liu¹

¹Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.

Cancer Informatics

|December 3, 2014

Summary

This summary is machine-generated.

Researchers evaluated statistical methods for splicing quantitative trait loci (sQTL) analysis in RNA-Seq data. Random effects meta-regression proved most effective, identifying sQTLs with high power and low false discovery rates.

Keywords:

RNA-Seq alternative splicing quantitative trait loci

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Published on: December 9, 2016

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Merging Absolute and Relative Quantitative PCR Data to Quantify STAT3 Splice Variant Transcripts

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Published on: October 9, 2016

Area of Science:

Genomics
Bioinformatics
Cancer Biology

Background:

Alternative splicing generates mRNA diversity, enabling different proteins from one gene.
Aberrant alternative splicing in cancer drives disease progression.
Understanding splicing misregulation can reveal new cancer drug targets.

Purpose of the Study:

To evaluate statistical methods for splicing quantitative trait loci (sQTL) analysis.
To compare random effects meta-regression, beta regression, and generalized linear mixed effects models.
To identify the most reliable method for sQTL analysis in RNA-Seq data.

Main Methods:

Utilized exon-inclusion levels estimated by the PennSeq algorithm.
Applied random effects meta-regression, beta regression, and generalized linear mixed effects models.
Compared these methods with GLiMMPS using simulated and real RNA-Seq datasets.

Main Results:

Random effects meta-regression demonstrated superior reliability and power.
This method identified sQTLs with low false discovery rates.
It outperformed GLiMMPS in identifying significant sQTLs.

Conclusions:

Three statistical methods for sQTL analysis in RNA-Seq were evaluated.
Random effects meta-regression is recommended as a powerful and reliable approach.
Findings will guide researchers in selecting appropriate statistical methods for sQTL analysis.